Gastroesophageal adenocarcinoma (GEC) remains a challenging problem in oncology. GEC remains the fourth most common malignancy and the second most common cause of death worldwide. Gastroesophageal junction (GEJ) adenocarcinomas have an estimated 350% increase in incidence in the US in the last two to three decades for unclear reasons. GEC is a molecularly heterogeneous disease both between patients (inter-patient) and within an individual patient (intra-patient). Intra-patient heterogenety manifests through space (primary tumor to metastatic lymph nodes to distant metastases, and even across different metastases) and time (natural selection of genomic aberrations conferring growth/metastatic advantage, as well as evolution of treatment resistant clones over time). Inter- and intra-patient tumor heterogeneity has likely contributed to negative results in a number of recent clinical trials testing novel molecularly targeted therapeutics using a 'one-size-fits-all' approach. Tumor heterogeneity poses a significant hurdle to achieving personalized treatment, particularly when using standard/accepted clinical trial designs. This proposal seeks to address inter-patient tumor heterogeneity by assigning treatment based on predefined predictive molecular 'oncogenic driver' categories, namely, HER2, MET, FGFR2, EGFR/HER3, and KRAS/PI3K-like. These are the most frequently observed molecular categories within GEC cell lines and tumor tissues. A comprehensive molecular profiling of the tumor at diagnosis will be done on the primary tumor and a metastatic disease site (liver, lung, or peritoneum) at enrollment, and all patients will be assigned to one of five specific treatments based on their metastatic tumor molecular profile as assessed via a novel treatment assignment algorithm. [This treatment algorithm is a compromise between the vast number of potential treatment groups and the feasibility of conducting such a trial and acquiring the many investigational agents necessary.] Metastatic disease will be uniformly used to profile the tumor in order to address intra-patient tumor heterogeneity through space, which can account for an approximate 10-15% discordant rate, resulting in subset misclassification. Additionally, patients will have planned serial biopsies at each progression point to determine molecular evolution over time and treatment. The correlative science incorporated into this study design will greatly improve our understanding of the disease with respect to inter-patient and intra-patient heterogeneity, and also will help to shed light on how to best address these hurdles in order to truly treat with molecular therapies for specific molecular targets, despite each molecular category occurring relatively infrequently. The feasibility and safety endpoints of this novel [pilot trial] are accompanied by a preliminary efficacy endpoint of overall survival [for the HER2+ and MET+ subgroups (N=68)], as compared to recent historical controls of approximately 12 months as seen in these GEC patients. [Secondary endpoints will include analysis of overall survival and other clinical endpoints amongst all five subgroups, anticipated to be approximately 104 patients]. This clinical trial design is innovative with its biostastistical approach and in its atempt to improve our understanding of the molecular biology of the disease, address inter- and intra-patient tissue heterogeneity within the disease, and to achieve our ultimate goal of molecularly personalized cancer care in order to significantly improve clinical outcomes.